1. Field of the Invention
This invention relates to a pattern pre-processing system which is chiefly employed for the recognition of printed characters, hand-written characters, etc. More particularly, it relates to a pattern processing system which performs a noise suppressing processing between the filtering processings of a pattern edge enhancing processing and a smoothing processing.
2. Description of the Prior Art
An optical character reader for recognizing printed characters, hand-written characters etc. receives the characters by a pattern input device which is generally an image pickup tube, a flying spot scanner or the like.
However, in case where the characteristics of the image pickup equipment are inferior or where the conditions of photographing are inferior, a pattern obtained is usually blurry. The blurred pattern represented by g(x, y) is approximated by the convolution integral between a blur function b(x, y) and an original image f(x, y) as indicated by the following equation (1): EQU g(x, y) = .intg..intg.b(.tau..sub.x, .tau..sub.y).multidot.f(x - .tau..sub.x, y - .tau..sub.y) d.tau..sub.x d.tau..sub.y ( 1)
When Eq. (1) is subjected to the Fourier transform, the convolution integral is expressed by a product as in the following equation (2): EQU G(.omega..sub.x, .omega..sub.y) = B(.omega..sub.x, .omega..sub.y).multidot.F(.omega..sub.x, .omega..sub.y) (2)
where capital letters signify the corresponding functions as Fourier-transformed.
From Eq. (2), EQU F(.omega..sub.x, .omega..sub.y) = G(.omega..sub.x, .omega..sub.y)/B(.omega..sub.x, .omega..sub.y) (3)
is obtained. As an expedient for sharpening the pattern, therefore, a prior-art method has been known in which the blur function and the blurred pattern as presumed are respectively subjected to the Fourier transforms, the operation given by Eq. (3) is executed and lastly the inverse Fourier transform is executed.
In such processing, however, a noise on the pattern is prone to be enhanced. As a countermeasure against this drawback, there has hitherto been proposed a method which combines the processing of enhancing Fourier components existent on the original image in large quantities by the Wiener filtering and the processing indicated by Eq. (3).
This method is effective as the processing for a pattern, such as character pattern and finger print, which is supposed to be constructed of a substantially uniform line width. However, since such a procedure in the frequency domain requires a considerable amount of circuitry for its realization, a filtering processing in the spatial domain corresponding thereto is more practical. A weight distribution by a filter in the spatial domain for a Wiener filter is unidimensionally illustrated by a profile as in FIG. 1a. When the convolution integral is executed with such filter, a pattern of comparatively good quality is obtained. In some cases, however, the noise is not perfectly erased, and an appropriate threshold value for a clipping level does not exist.
More specifically, when a pattern having a shade distribution as shown in FIG. 2a is subjected to the filtering processing, a pattern of a shade distribution as shown in FIG. 2b is obtained. When this pattern is binary-coded by threshold values .theta..sub.1 and .theta..sub.2 as shown in FIG. 2b, the results become as illustrated in FIG. 2c and FIG. 2d, respectively. In the signal depicted in FIG. 2c, a significant signal component is erased, while in the signal depicted in FIG. 2d, a noise component remains. In FIG. 2a, N represents the noise component and SC.sub.1 -SC.sub.3 represent signals (the significant signal components). In FIG. 2c, the significant signal component SC.sub.3 is erased.
When it is intended to reduce the noise level by changing the profile or the weight distribution of the filter, the sharpness of the edge of a line is degraded. On the other hand, when it is intended to sharpen the edge of the line, the noise is enhanced. In this manner, a satisfactory result is difficult to attain.
It will be further explained that the noise is not reduced as described above. The filter W.sub.1 of the profile exhibiting the weight distribution as shown in FIG. 1a is decomposed into a filter W.sub.2 of a profile as shown in FIG. 1b and a filter W.sub.3 of a profile as shown in FIG. 1c. Accordingly, the following equation (4) holds. EQU W.sub.1 (x, y) = .intg..intg. W.sub.2 (.tau..sub.x, .tau..sub.y) .multidot. W.sub.3 (x - .tau..sub.x, y - .tau..sub.y)d.sub.x d.sub.y ( 4)
Therefore, the result of the filtering processing by the mask W.sub.1 agrees with a result obtained in such a way that the result of a filtering processing by the mask W.sub.2 is further subjected to a filtering processing by the mask W.sub.3. The mask W.sub.2 enhances the edge of the line, and simultaneously enhances the noise superposed on the pattern. The mask W.sub.3 has the characteristics of a smoothing filter, and has the effect of reducing the noise and the effect of resonating with a certain line width. The aforecited problem is attributed to the fact that the noise is not sufficiently reduced even by such smoothing filter.